Evidence map›Paper›PMID 38232254›Full record

ArticleJornal brasileiro de pneumologia : publicacao oficial da Sociedade Brasileira de Pneumologia e Tisilogia2024

Detection of subsolid nodules on chest CT scans during the COVID-19 pandemic.

Ana Paula Zanardo, Vicente Bohrer Brentano, Rafael Domingos Grando, Rafael Ramos Rambo, Felipe Teixeira Hertz, Luís Carlos Anflor Júnior, Jônatas Fávero Prietto Dos Santos, Gabriela Schneider Galvão, Cristiano Feijó Andrade

Open access · diamondAbstract read
In one paragraph

Article in Jornal brasileiro de pneumologia : publicacao oficial da Sociedade Brasileira de Pneumologia e Tisilogia, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.3field-weighted citation impact, top 33% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. Subsolid nodules, adenocarcinomas, and COVID-19: looking for needles in a haystack.Jornal brasileiro de pneumologia : publicacao oficial da Sociedade Brasileira de Pneumologia e Tisilogia · 2024
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors at 3 institutions in 2 countries.

Ana Paula Zanardo. Programa de Pós-Graduação em Ciências Pneumológicas, Universidade Federal do Rio Grande do Sul, Porto Alegre (RS) Brasil.ORCID 0000-0002-1398-9925
Vicente Bohrer Brentano. Departamento de Radiologia, Hospital Moinhos de Vento, Porto Alegre (RS) Brasil.ORCID 0000-0003-1825-1732
Rafael Domingos Grando. Programa de Pós-Graduação em Ciências Pneumológicas, Universidade Federal do Rio Grande do Sul, Porto Alegre (RS) Brasil.ORCID 0000-0003-4189-3557
Rafael Ramos Rambo. Programa de Pós-Graduação em Ciências Pneumológicas, Universidade Federal do Rio Grande do Sul, Porto Alegre (RS) Brasil.ORCID 0000-0002-0559-7972
Felipe Teixeira Hertz. Departamento de Radiologia, Hospital Moinhos de Vento, Porto Alegre (RS) Brasil.ORCID 0000-0003-3745-8341
Luís Carlos Anflor Júnior. Departamento de Radiologia, Hospital Moinhos de Vento, Porto Alegre (RS) Brasil.ORCID 0000-0001-5530-0834
Jônatas Fávero Prietto Dos Santos. Programa de Pós-Graduação em Ciências Pneumológicas, Universidade Federal do Rio Grande do Sul, Porto Alegre (RS) Brasil.ORCID 0000-0002-2896-5707
Gabriela Schneider Galvão. Programa de Pós-Graduação em Ciências Pneumológicas, Universidade Federal do Rio Grande do Sul, Porto Alegre (RS) Brasil.ORCID 0000-0001-9597-8658
Cristiano Feijó Andrade. Serviço de Cirurgia Torácica e Pulmonar, Hospital Moinhos de Vento, Porto Alegre (RS) Brasil.ORCID 0000-0002-6431-3079
Universidade Federal do Rio Grande do Sul · BRHospital Moinhos de Vento · BRUniversidade Federal de Ciências da Saúde de Porto Alegre · BR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo investigate the detection of subsolid nodules (SSNs) on chest CT scans of outpatients before and during the COVID-19 pandemic, as well as to correlate the imaging findings with epidemiological data. We hypothesized that (pre)malignant nonsolid nodules were underdiagnosed during the COVID-19 pandemic because of an overlap of imaging findings between SSNs and COVID-19 pneumonia.

methodsThis was a retrospective study including all chest CT scans performed in adult outpatients (> 18 years of age) in September of 2019 (i.e., before the COVID-19 pandemic) and in September of 2020 (i.e., during the COVID-19 pandemic). The images were reviewed by a thoracic radiologist, and epidemiological data were collected from patient-filled questionnaires and clinical referrals. Regression models were used in order to control for confounding factors.

resultsA total of 650 and 760 chest CT scans were reviewed for the 2019 and 2020 samples, respectively. SSNs were found in 10.6% of the patients in the 2019 sample and in 7.9% of those in the 2020 sample (p = 0.10). Multiple SSNs were found in 23 and 11 of the patients in the 2019 and 2020 samples, respectively. Women constituted the majority of the study population. The mean age was 62.8 ± 14.8 years in the 2019 sample and 59.5 ± 15.1 years in the 2020 sample (p < 0.01). COVID-19 accounted for 24% of all referrals for CT examination in 2020.

conclusionsFewer SSNs were detected on chest CT scans of outpatients during the COVID-19 pandemic than before the pandemic, although the difference was not significant. In addition to COVID-19, the major difference between the 2019 and 2020 samples was the younger age in the 2020 sample. We can assume that fewer SSNs will be detected in a population with a higher proportion of COVID-19 suspicion or diagnosis.

Indexed as

COVID-19Lung NeoplasmsMultiple Pulmonary NodulesAdultAgedFemaleHumansMiddle AgedPandemicsRetrospective StudiesTomography, X-Ray Computed

Identifiers

PMID38232254
PMCPMC10769470
OpenAlexW4389671160

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.